MLA-C01 ML Model Development Practice Question
A company is fine-tuning a large language model using LoRA with a Hugging Face estimator in SageMaker. They want to reduce memory usage during training. Which instance type is most cost-effective for this workload?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ml.g5.xlarge
LoRA reduces the number of trainable parameters, allowing training on smaller GPUs. ml.g5 instances are optimized for machine learning inference and training with a good price-performance for fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ml.p4d.24xlarge
Why it's wrong here
P4d instances are powerful but expensive; they are overkill for LoRA fine-tuning.
- ✓
ml.g5.xlarge
Why this is correct
G5 instances are cost-effective for fine-tuning with LoRA, providing good performance at lower cost.
- ✗
ml.c5.2xlarge
Why it's wrong here
C5 instances are compute-optimized but lack GPU, which is needed for efficient LLM fine-tuning.
- ✗
ml.trn1.2xlarge
Why it's wrong here
Trn1 instances are optimized for training large models but may be costlier than necessary for LoRA fine-tuning.
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